Optimization of TIG Welding Parameters Using a Hybrid Nelder Mead-Evolutionary Algorithms Method

A number of evolutionary algorithms such as genetic algorithms, simulated annealing, particle swarm optimization, etc., have been used by researchers in order to optimize different manufacturing processes. In many cases these algorithms are either incapable of reaching global minimum or the time and...

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Veröffentlicht in:Journal of Manufacturing and Materials Processing Jg. 4; H. 1; S. 10
Hauptverfasser: Kshirsagar, Rohit, Jones, Steve, Lawrence, Jonathan, Tabor, Jim
Format: Journal Article
Sprache:Englisch
Veröffentlicht: MDPI AG 01.03.2020
Schlagworte:
Algorithms
bead geometry optimization
Gas welding
genetic algorithm
Mathematical models
nelder-mead optimization
Optimization theory
particle swarm optimization
simulated annealing
tig welding
Iran
ISSN:2504-4494, 2504-4494
Online-Zugang:Volltext
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Zusammenfassung:A number of evolutionary algorithms such as genetic algorithms, simulated annealing, particle swarm optimization, etc., have been used by researchers in order to optimize different manufacturing processes. In many cases these algorithms are either incapable of reaching global minimum or the time and computational effort (function evaluations) required makes the application of these algorithms impractical. However, if the Nelder Mead optimization method is applied to approximate solutions cheaply obtained from these algorithms, the solution can be further refined to obtain near global minimum of a given error function within only a few additional function evaluations. The initial solutions (vertices) required for the application of Nelder-Mead optimization can be obtained through multiple evolutionary algorithms. The results obtained using this hybrid method are better than that obtained from individual algorithms and also show a significant reduction in the computation effort.
ISSN:2504-4494
2504-4494
DOI:10.3390/jmmp4010010